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Evaluating inter-individual variability captured by the Eleveld pharmacokinetics model.
- Source :
-
Journal of clinical monitoring and computing [J Clin Monit Comput] 2024 Apr; Vol. 38 (2), pp. 505-518. Date of Electronic Publication: 2023 Nov 07. - Publication Year :
- 2024
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Abstract
- Inter-individual variability in Pharmacokinetic (PK) and Pharmacodynamic (PD) models significantly affects the accuracy of Target Controlled Infusion and closed-loop control of anesthesia. We hypothesize that the novel Eleveld PK model captures more inter-individual variability relevant to both open-loop and closed-loop control design, resulting in reduced variability in PD models identified using the Eleveld PK model's plasma prediction compared to the Schuttler or Schnider PK model. We used a dataset of propofol infusion rates and Depth of Hypnosis measurements across three demographic groups: elderly, obese, and adult. PD models are identified based on plasma concentration prediction using three PK models (Schuttler, Schnider, and Eleveld). Validation methods are presented to confirm acceptable predictive performance and comparable PK-PD model variability within each demographic group. To test our hypothesis, we compared coefficient variations in step responses for open-loop control and multiplicative uncertainty of PD model sets for closed-loop control. Validated PKPD models using the Schuttler and Schnider PK model showed no significant differences in predictive response and multiplicative uncertainty compared to the Eleveld PK model. The coefficient variations in step responses of PD model sets and the frequency ranges, corresponding to uncertainty below one, were comparable for all three PK models. The comparison of the accumulated coefficient of variation in the step-response and the uncertainty of the PD model sets indicated that the Eleveld PK model does not offer any advantage for the design of open-loop or closed-loop control of anesthesia.<br /> (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)
Details
- Language :
- English
- ISSN :
- 1573-2614
- Volume :
- 38
- Issue :
- 2
- Database :
- MEDLINE
- Journal :
- Journal of clinical monitoring and computing
- Publication Type :
- Academic Journal
- Accession number :
- 37934309
- Full Text :
- https://doi.org/10.1007/s10877-023-01083-5